Pandas使用tips集锦
1. 替换字符
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| auto['horsepower'] = auto['horsepower'].replace('?',np.nan)
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2. 删除空值
3. 改变字段类型
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| auto['horsepower'] = auto['horsepower'].astype('int')
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4. 按照某一个/多个字段排序
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| auto = auto.sort_values(by = ['horsepower'],ascending = True, axis = 0)
boston.sort_values(by= ['CRIM','TAX','PTRATIO'],ascending=False).head().index
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5. pandas设置索引
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| college = college.set_index(['Unnamed: 0'], append=True, verify_integrity=True) college.rename_axis([None, 'Name'], inplace=True)
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6. 对某一列进行判断并且置值
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| college['Elite'] = np.where(college['Top10perc'] > 50,'Yes','No')
# 使用map的方式
credit['Student2'] = credit.Student.map({'No':0, 'Yes':1})
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7. 对某一列的值进行计数
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| college['Elite'].value_counts()
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8. 对某一列的值进行筛选
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| elite_colleges = college[college['Elite'] == 'Yes']
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9. 对某一列的数值进行分桶操作
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| college['Enroll'] = pd.cut(college['Enroll'], bins=3, labels = ['Low','Medium','High'])
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10. 查看df中的各个字段的unique的数目和字段类型信息等
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| auto.nunique()
auto.info()
auto['horsepower'].unique() # 看某一列的unique数目
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11. 筛选单列和多列
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| info['range'] = info['max'] - info['min']
info = info[['mean','range','std']]
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12. 根据index筛选和删除多列
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| info = auto.drop(auto.index[10:85]).describe().T
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13. load数据并且指定列名
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| boston = pd.DataFrame(load_boston().data,columns = load_boston().feature_names )
data = pd.DataFrame(boston.data,columns = boston['feature_names'])
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14. 通过iloc选取行和列
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| corr_matrix = boston.corr()
corr_matrix.iloc[1:,0].sort_values()
# 选取所有行，和从第二列开始的所有列
data = data.iloc[:,1:]
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15. 按照行/列进行df的拼接
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| # 按行拼接
features = pd.concat([constant,features],axis = 1)
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16. load数据并且选定列
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| credit = pd.read_csv('Data/Credit.csv', usecols=list(range(1,12)))
advertising = pd.read_csv('Data/Advertising.csv', usecols=[1,2,3,4])
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17. 读取数据的时候，就设置空值的表示方法
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| auto = pd.read_csv('Data/Auto.csv', na_values='?').dropna()
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18. 构造pandas的df
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| X = np.random.normal(size = 100)
y = np.random.permutation(X)
data = pd.DataFrame({'X':X,'y':y})
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19. 读取文本文件的时候，设置某列为索引列
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| data = pd.read_csv('../data/Smarket.csv',index_col=0)
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20. 原地替换
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| df_x.replace(to_replace={0:'No', 1:'Yes', 'True':'Yes', 'False':'No'}, inplace=True)
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参考链接